4.3.2 模型构建
上一节定义好已经了解了ResNet模型结构，本节直接使用飞桨高层API中的Resnet50进行图像分类实验。
In [7]
from paddle.vision.models import resnet50 model = resnet50()
W0714 20:32:55.131150 102 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W0714 20:32:55.136173 102 device_context.cc:465] device: 0, cuDNN Version: 7.6.
4.3.3 损失函数
飞桨高层API中都为大家提供了实现好交叉熵损失函数，代码如下所示。
In [8]
import paddle.nn.functional as F loss_fn = F.cross_entropy
4.3.4 模型训练
使用交叉熵损失函数，并用SGD作为优化器来训练ResNet网络。
In [9]
# -*- coding: utf-8 -*- # LeNet 识别眼疾图片 import os import random import paddle import numpy as np class Runner(object): def __init__(self, model, optimizer, loss_fn): self.model = model self.optimizer = optimizer self.loss_fn = loss_fn # 记录全局最优指标 self.best_acc = 0 # 定义训练过程 def train_pm(self, train_datadir, val_datadir, **kwargs): print('start training ... ') self.model.train() num_epochs = kwargs.get('num_epochs', 0) csv_file = kwargs.get('csv_file', 0) save_path = kwargs.get("save_path", "/home/aistudio/output/") # 定义数据读取器，训练数据读取器 train_loader = data_loader(train_datadir, batch_size=10, mode='train') for epoch in range(num_epochs): for batch_id, data in enumerate(train_loader()): x_data, y_data = data img = paddle.to_tensor(x_data) label = paddle.to_tensor(y_data) # 运行模型前向计算，得到预测值 logits = model(img) avg_loss = self.loss_fn(logits, label) if batch_id % 20 == 0: print("epoch: {}, batch_id: {}, loss is: {:.4f}".format(epoch, batch_id, float(avg_loss.numpy()))) # 反向传播，更新权重，清除梯度 avg_loss.backward() self.optimizer.step() self.optimizer.clear_grad() acc = self.evaluate_pm(val_datadir, csv_file) self.model.train() if acc > self.best_acc: self.save_model(save_path) self.best_acc = acc # 模型评估阶段，使用'paddle.no_grad()'控制不计算和存储梯度 @paddle.no_grad() def evaluate_pm(self, val_datadir, csv_file): self.model.eval() accuracies = [] losses = [] # 验证数据读取器 valid_loader = valid_data_loader(val_datadir, csv_file) for batch_id, data in enumerate(valid_loader()): x_data, y_data = data img = paddle.to_tensor(x_data) label = paddle.to_tensor(y_data) # 运行模型前向计算，得到预测值 logits = self.model(img) # 多分类，使用softmax计算预测概率 pred = F.softmax(logits) loss = self.loss_fn(pred, label) acc = paddle.metric.accuracy(pred, label) accuracies.append(acc.numpy()) losses.append(loss.numpy()) print("[validation] accuracy/loss: {:.4f}/{:.4f}".format(np.mean(accuracies), np.mean(losses))) return np.mean(accuracies) # 模型评估阶段，使用'paddle.no_grad()'控制不计算和存储梯度 @paddle.no_grad() def predict_pm(self, x, **kwargs): # 将模型设置为评估模式 self.model.eval() # 运行模型前向计算，得到预测值 logits = self.model(x) return logits def save_model(self, save_path): paddle.save(self.model.state_dict(), save_path + 'palm.pdparams') paddle.save(self.optimizer.state_dict(), save_path + 'palm.pdopt') def load_model(self, model_path): model_state_dict = paddle.load(model_path) self.model.set_state_dict(model_state_dict)
实例化Runner类，并传入训练配置，代码实现如下：
In [12]
# 开启0号GPU训练 use_gpu = True paddle.device.set_device('gpu:0') if use_gpu else paddle.device.set_device('cpu') # 定义优化器 # opt = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9, parameters=model.parameters(), weight_decay=0.001) opt = paddle.optimizer.SGD(learning_rate=0.001, parameters=model.parameters()) runner = Runner(model, opt, loss_fn)
用Runner在训练集上训练5个epoch，并保存准确率最高的模型作为最佳模型。
In [13]
import os # 数据集路径 DATADIR = '/home/aistudio/work/palm/PALM-Training400/PALM-Training400' DATADIR2 = '/home/aistudio/work/palm/PALM-Validation400' CSVFILE = '/home/aistudio/labels.csv' # 设置迭代轮数 EPOCH_NUM = 5 # 模型保存路径 PATH='/home/aistudio/output/' if not os.path.exists(PATH): os.makedirs(PATH) # 启动训练过程 runner.train_pm(DATADIR, DATADIR2, num_epochs=EPOCH_NUM, csv_file=CSVFILE, save_path=PATH)
start training ...
epoch: 0, batch_id: 0, loss is: 0.3287
epoch: 0, batch_id: 20, loss is: 0.0716
[validation] accuracy/loss: 0.9625/5.9818
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/framework/io.py:729: UserWarning: The input state dict is empty, no need to save.
warnings.warn("The input state dict is empty, no need to save.")
epoch: 1, batch_id: 0, loss is: 0.1286
epoch: 1, batch_id: 20, loss is: 0.4718
[validation] accuracy/loss: 0.9650/5.9824
epoch: 2, batch_id: 0, loss is: 0.0892
epoch: 2, batch_id: 20, loss is: 0.0313
[validation] accuracy/loss: 0.9625/5.9801
epoch: 3, batch_id: 0, loss is: 0.1362
epoch: 3, batch_id: 20, loss is: 0.0569
[validation] accuracy/loss: 0.9625/5.9746
epoch: 4, batch_id: 0, loss is: 0.1036
epoch: 4, batch_id: 20, loss is: 0.0873
[validation] accuracy/loss: 0.9575/5.9856
通过运行结果可以发现，使用ResNet在眼疾筛查数据集iChallenge-PM上，经过5个epoch的训练，在验证集上的准确率可以达到96%左右。
4.3.5 模型评估
使用测试数据对在训练过程中保存的最佳模型进行评价，观察模型在评估集上的准确率。代码实现如下：
In [ ]
# 加载最优模型 runner.load_model('/home/aistudio/output/palm.pdparams') # 模型评价 score = runner.evaluate_pm(DATADIR2, CSVFILE)
[validation] accuracy/loss: 0.9725/5.9591
4.3.6 模型预测
同样地，也可以使用保存好的模型，对测试集中的某一个数据进行模型预测，观察模型效果。代码实现如下：
In [18]
import cv2 from PIL import Image import matplotlib.pyplot as plt import paddle import paddle.nn.functional as F %matplotlib inline # 加载最优模型 runner.load_model('/home/aistudio/output/palm.pdparams') # 获取测试集中第一条数据 DATADIRv2 = '/home/aistudio/work/palm/PALM-Validation400' filelists = open('/home/aistudio/labels.csv').readlines() # 可以通过修改filelists列表的数字获取其他测试图片，可取值1-400 line = filelists[1].strip().split(',') name, label = line[1], int(line[2]) # 读取测试图片 img = cv2.imread(os.path.join(DATADIRv2, name)) # 测试图片预处理 trans_img = transform_img(img) unsqueeze_img = paddle.unsqueeze(paddle.to_tensor(trans_img), axis=0) # 模型预测 logits = runner.predict_pm(unsqueeze_img) result=F.softmax(logits) pred_class = paddle.argmax(result).numpy() # 输出真实类别与预测类别 print("The true category is {} and the predicted category is {}".format(label, pred_class)) # 图片可视化 show_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.imshow(show_img) plt.show()
The true category is 0 and the predicted category is [0]
小结
在这一节里，我们通过ResNet模型实现眼疾识别，在验证集上的预测精度在95%左右，通过这个案例熟悉了基础的视觉任务构建流程。如果读者有兴趣的话，可以进一步调整学习率和训练轮数等超参数，观察是否能够得到更高的精度。
作业
本节通过调用飞桨高层API Resnet50模型from paddle.vision.models import resnet50实现了眼疾识别。更换其他模型，看是否能得到更高的精度，
